Volumetric CNNs or Voxelization is another approach widely used in the literature. VoxNet [1]} created a volumetric Occupancy Grid representation with a supervised 3D Convolutional Neural Network. In 3D shapeNets [2]}, a convolutional deep belief network was proposed to represent a geometric 3D shape on a 3D voxel grid to recognize and reconstruct objects from a single-view 2.5D depth map. Bo Li [3]} trained a 3D FCN to detect vehicles on KITTI [4]} Lidar data. Charles et. al [5]} proposed a 3D CNN trained along with an image-based 2D Network In Network(NIN) to classify the 2D projection of the original 3D shape. Multi-view CNN [6]} introduced the usage of different views parsed with a CNN to extract view-based features followed by a view-pooling to obtain the overall classification. Most of these approaches are computationally-expensive due to using 3D convolutional layers and operating on (around 90%) sparse voxel grid cells.
